Introduction to the Special Issue: TransdisciplinaryApproaches to Language Learning and Teaching in Transnational Times
Bibliographic record
Abstract
The initial ideas for this special issue transpired from an invited symposium panel, “Interdisciplinary Approaches for Language Teaching and Learning in Contemporary and Transnational Spaces,” that I organized for the 2014 AILA (Association Internationale de la Linguistique Appliquée) World Congress in Brisbane, Australia. With the acceleration of globalization, mobility, technological change, and the continued rise of youth with transnational identities and complex linguistic practices, I was compelled to talk about the need for interdisciplinary approaches that would inform language education in transnational times. Two years later, through several drafts and deliberations that went into this issue for L2 Journal, and with sensitivity to the current shifts in the field of applied linguistics, I have found that transdisciplinary represents a more appropriate fit for language learning and teaching in contemporary times. Building upon the recent article by the Douglas Fir Group (2016), “A transdisciplinary framework for SLA in a multilingual world,” and taking account of some of the current initiatives in the literature that seek to open up the teaching of national standard languages to sociolinguistic variation, translation practices, multimodal activities, and translanguaging (e.g., Canagarajah, 2011; García & Wei, 2014 Kern, 2011; Kramsch & Malinowski, 2014), this special issue seeks to broaden these efforts further. This increase in scope is achieved by weaving together a unique, integrative conceptualization of transdisciplinarity—one that not only seeks to foster critical reflexive awareness (Byrd Clark & Dervin, 2014) of the tensions that exist among the real life complexities in our learning and teaching experiences, but that additionally makes visible the unpredictable, multidimensional, and multiple ways in which we make meaning (including the varied ways in which we, the authors, have come to make sense of transdisciplinarity). Accordingly, this special issue embodies a postmodern, ecological, and relativistic stance when it comes to transdisciplinary approaches concerning interculturality and the study of language in use. Such a stance is highly relevant as the global realities of our times continue to put into question claims of belonging to ‘imagined communities’ (see Anderson, 1991) of clearly demarcated nation-states unified through a national language (one language=one nation=one culture), with native speakers representative of a homogeneous speech community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.068 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".